Learning Outcomes:
This course provides a comprehensive introduction to quantum computing, exploring its principles and applications in machine learning and optimization. Beginning with the foundational postulates of quantum mechanics, it establishes the theoretical framework necessary to understand quantum systems. The course then delves into variational circuits as machine learning methods, covering quantum neural networks, data encoding, and training techniques. It further explores quantum models as kernel methods, including optimization techniques such as the Variational Quantum Eigensolver and the Quantum Approximate Optimization Algorithm. Finally, the course examines potential quantum advantages, such as quantum annealing and Monte Carlo methods, with practical applications in finance and simulation.
